Data Mining Techniques in Knowledge Management

Introduction

Knowledge management (KM) as an organizational innovation has been with us for more than a decade. As a discipline, it has reached a state of maturity where we can now discern the principles, practices, and tools that make it unique. As a discourse, it has engendered new concepts and categories for us to make sense of the many important ways that organizations use knowledge to create value. Given the richness of ideas and innovations that have emerged under the rubric of knowledge management, and given the tremendous interest in schools and organizations to learn about the subject, it is something of a mystery that there are so few textbooks available. Perhaps it is because the field draws upon a wide range of subject areas, or perhaps it is because many different perspectives complicate the discussion of issues that engage knowledge management.

The ability to manage knowledge is becoming increasingly more crucial in today’s knowledge economy. The creation and diffusion of knowledge have become ever more important factors in competitiveness. More and more, knowledge is being regarded as a valuable commodity that is embedded in products (especially high-technology products) and in the tacit knowledge of highly mobile employees. Although knowledge is increasingly being viewed as a commodity or an intellectual asset, it possesses some paradoxical characteristics that are radically different from those of other valuable commodities.

These knowledge characteristics include the following:

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  Use of knowledge does not consume it.

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  Transferral of knowledge does not result in losing it.

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  Knowledge is abundant, but the ability to use it is scarce.

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  Much of an organization’s valuable knowledge walks out the door at the end of the day.

Knowledge management (KM) was initially defined as the process of applying a systematic approach to the capture, structure, management, and dissemination of knowledge throughout an organization in order to work faster, reuse best practices, and reduce costly rework from project to project. Knowledge management solutions have proven to be most successful in the capture, storage, and subsequent dissemination of knowledge that has been rendered explicit—particularly lessons learned and best practices.

Many knowledge management (KM) efforts have been largely concerned with capturing, codifying, and sharing the knowledge held by people in organizations. Although there is still a lack of consensus over what constitutes a good definition of KM

A good definition of knowledge management incorporates both the capturing and storing of the knowledge perspective, together with the valuing of intellectual assets. For example:

Knowledge management is the deliberate and systematic coordination of an organization’s people, technology, processes, and organizational structure in order to add value through reuse and innovation. This coordination is achieved through creating, sharing, and applying knowledge as well as through feeding the valuable lessons learned and best practices into corporate memory in order to foster continued organizational learning.

Data Mining And Knowledge Management

The purpose of data mining is knowledge discovery. It extracts hidden information from large databases and hence is a powerful technology with a great potential for companies to focus on the analysis of the stored database.

The terms of Knowledge discovery and data mining are different. Knowledge discovery refers to the overall process of discovering useful knowledge from data. Data mining refers to discover new patterns from a wealth of data in databases by focusing on the algorithms to extract useful knowledge.